Owen Marschall
@omarschall.bsky.social
Postdoc in the Litwin-Kumar lab at the Center for Theoretical Neuroscience at Columbia University.
I'm interested in multi-tasking and dimensionality.
I'm interested in multi-tasking and dimensionality.
1/X Excited to present this preprint on multi-tasking, with
@david-g-clark.bsky.social and Ashok Litwin-Kumar! Timely too, as “low-D manifold” has been trending again. (If you read thru the end, we escape Flatland and return to the glorious high-D world we deserve.) www.biorxiv.org/content/10.6...
@david-g-clark.bsky.social and Ashok Litwin-Kumar! Timely too, as “low-D manifold” has been trending again. (If you read thru the end, we escape Flatland and return to the glorious high-D world we deserve.) www.biorxiv.org/content/10.6...
A theory of multi-task computation and task selection
Neural activity during the performance of a stereotyped behavioral task is often described as low-dimensional, occupying only a limited region in the space of all firing-rate patterns. This region has...
www.biorxiv.org
December 15, 2025 at 7:41 PM
1/X Excited to present this preprint on multi-tasking, with
@david-g-clark.bsky.social and Ashok Litwin-Kumar! Timely too, as “low-D manifold” has been trending again. (If you read thru the end, we escape Flatland and return to the glorious high-D world we deserve.) www.biorxiv.org/content/10.6...
@david-g-clark.bsky.social and Ashok Litwin-Kumar! Timely too, as “low-D manifold” has been trending again. (If you read thru the end, we escape Flatland and return to the glorious high-D world we deserve.) www.biorxiv.org/content/10.6...
Can confirm this was a fun project! My favorite takeaway is that the (low-but-extensive) rank of a network can be used as a knob for controlling dimensionality while leaving single-neuron properties unchanged.
Now in PRX: Theory linking connectivity structure to collective activity in nonlinear RNNs!
For neuro fans: conn. structure can be invisible in single neurons but shape pop. activity
For low-rank RNN fans: a theory of rank=O(N)
For physics fans: fluctuations around DMFT saddle⇒dimension of activity
For neuro fans: conn. structure can be invisible in single neurons but shape pop. activity
For low-rank RNN fans: a theory of rank=O(N)
For physics fans: fluctuations around DMFT saddle⇒dimension of activity
Connectivity Structure and Dynamics of Nonlinear Recurrent Neural Networks
The structure of brain connectivity predicts collective neural activity, with a small number of connectivity features determining activity dimensionality, linking circuit architecture to network-level...
journals.aps.org
November 4, 2025 at 3:53 PM
Can confirm this was a fun project! My favorite takeaway is that the (low-but-extensive) rank of a network can be used as a knob for controlling dimensionality while leaving single-neuron properties unchanged.
Reposted by Owen Marschall
Wanted to share a new version (much cleaner!) of a preprint on how connectivity structure shapes collective dynamics in nonlinear RNNs. Neural circuits have highly non-iid connectivity (e.g., rapidly decaying singular values, structured singular-vector overlaps), unlike classical random RNN models.
Connectivity structure and dynamics of nonlinear recurrent neural networks
Studies of the dynamics of nonlinear recurrent neural networks often assume independent and identically distributed couplings, but large-scale connectomics data indicate that biological neural circuit...
arxiv.org
August 19, 2025 at 3:42 PM
Wanted to share a new version (much cleaner!) of a preprint on how connectivity structure shapes collective dynamics in nonlinear RNNs. Neural circuits have highly non-iid connectivity (e.g., rapidly decaying singular values, structured singular-vector overlaps), unlike classical random RNN models.